Characterization of Zinc oxide & Aluminum Ferrite and Simulation studies of M-H plots of Cobalt/Cobaltoxide

Zinc oxide and Aluminum Ferrite were prepared Chemical route. The samples were characterized by XRD and VSM. Simulation of M-H plots of Co/CoO thin films were performed. Effect of parameters was observed on saturation magnetization.Comment: Working paper (11 pages, 8 figures

DESIGN, OPTIMIZATION, AND STATISTICAL EVALUATION OF ORAL RECONSTITUTED SUSPENSION OF CLARITHROMYCIN USING ION-EXCHANGE RESINS

Objectives: The objective of the present study is taste masking of bitter clarithromycin using Indion 204, Indion 234, and Tulsion 335 as ion-exchange resins, which forms insoluble complexes, inhibiting the drug release in saliva as ion-exchange resins are cross-linked polymers, water-insoluble that contains salt-forming groups in repeating positions on the polymer chain. Drugs that are bitter and cationic get adsorbed onto weak cationic exchange resins of carboxylic acid functionality such as Indion 204, Indion 234, and Tulsion 335 to form non-bitter complexes. Methods: The drug-resin complex loading process was optimized for the resin content, activation, swelling time, stirring time, influence of pH, and temperature for maximum drug loading and the formed complex was evaluated by differential scanning calorimetry (DSC) to confirm complex formation. The drug-resin complex was also characterized by their micromeritic and rheological properties. These complexes were used to prepare oral reconstituted suspensions and the taste was evaluated. The formulation was evaluated for various parameters such as sedimentation volume, pH, redispersibility, viscosity, drug content, and in vitro drug release. Results: Acid-activated resins comprising Indion 204, Indion 234, and Tulsion 335 with the drug:resin ratio of 1:2, stirred in a solution of pH 7–8 at 70° for 6 h had a maximum drug loading and masked the bitter taste of clarithromycin. DSC of the drug-resin complex (DRC) revealed that there was interaction leading to complex formation. The drug-resin complex was formulated into suspension formulations (S1-S9) and evaluated. Various parameters were found to be within permissible limits. Formulations S3, S6, and S9 containing 1:2 ratios of the drug-resin complex of Indion 204, Indion 234, and Tulsion 335 revealed maximum taste masking. This was further confirmed by treatment of taste evaluation scores obtained from the volunteers by ANOVA, Dunnett’s multiple comparison test, and Tukey’s multiple comparison test. All the three optimized formulations had a significant difference of p<0.001 when compared to control S10. S6 formulation was widely accepted. Conclusion: Ion-exchange complexation could efficiently mask the bitter taste of clarithromycin and achieve palatable taste suitable for pediatric use

A PRESPECTIVE STUDY ON APOBEC PROTEIN FAMILY

Apobec is an apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like" is a family of deaminases. It has two types of APOBEC enzymes n-terminal of apobec enzyme and C-terminal of APOBEC enzyme. N-terminal domain is catalytic domain and C-terminal domain is a pseudo catalytic domain. Pathogen and cellular genome undergo mutation by human DNA cytosine to uracil deaminases. Three subtypes of APOBEC3D. APOBEC3F, APOBEC3G and APOBEC3H restrict human deficiency virus-1. Two APOBEC enzymes are the sources of somatic mutagenesis in cancer cells that drive tumor evolution and manifest clinically as theraphy resistance. This review of the APOBEC family will focus on an open question in regulation, namely what role the interactions of these proteins with RNA have in editing substrate recognition or allosteric regulation of DNA mutagenic and host-defense activities

Accurate monitoring and fault detection in wind measuring devices through wireless sensor networks

Many wind energy projects report poor performance as low as 60% of the predicted performance. The reason for this is poor resource assessment and the use of new untested technologies and systems in remote locations. Predictions about the potential of an area for wind energy projects (through simulated models) may vary from the actual potential of the area. Hence, introducing accurate site assessment techniques will lead to accurate predictions of energy production from a particular area. We solve this problem by installing a Wireless Sensor Network (WSN) to periodically analyze the data from anemometers installed in that area. After comparative analysis of the acquired data, the anemometers transmit their readings through a WSN to the sink node for analysis. The sink node uses an iterative algorithm which sequentially detects any faulty anemometer and passes the details of the fault to the central system or main station. We apply the proposed technique in simulation as well as in practical implementation and study its accuracy by comparing the simulation results with experimental results to analyze the variation in the results obtained from both simulation model and implemented model. Simulation results show that the algorithm indicates faulty anemometers with high accuracy and low false alarm rate when as many as 25% of the anemometers become faulty. Experimental analysis shows that anemometers incorporating this solution are better assessed and performance level of implemented projects is increased above 86% of the simulated models

MBFRDH: Design of a Multimodal Bioinspired Feature Representation Deep Learning Model for Identification of Heart-Diseases

Electrocardiograms (ECGs) are generated by checking different beating patterns of heart, and are widely used for identification of multiple heart-related issues. Existing deep learning models that are proposed for ECG analysis are either highly complex, or showcase lower scalability when applied to clinical scans. To overcome these issues, this text proposes design of a novel multimodal bioinspired feature representation deep learning model for identification of heart-diseases. The proposed model initially collects large-scale ECG datasets, and extracts Fourier, Cosine, iVector, Gabor, and Wavelet components. These components are given to a Grey Wolf Optimization (GWO) based feature selection model, which assists in identification of high-inter-class variance feature sets. This is done via modelling a variance-based fitness function and fusing it with an Iterative Learning Model (ILM) that use feedback-accuracy levels for optimization of selected feature sets. The extracted features are used to incrementally train a custom 1D Binary-Augmented Convolutional Neural Network (1D BACNN) that can be trained for multiclass scenarios. The BACNN Model is trained individually for each of the heart diseases. Each BACNN categorizes input ECG samples between ‘Normal’, and ‘Heart-Disease’ categories. Due to use of this binary-type classification, the proposed model is able to achieve a consistent 99.9% accuracy for multiple heart disease sets, which is found to be higher than most of the existing multiclass techniques. The model was tested for Angina, Arrhythmia, Valve disease, and Congenital heart conditions, and was observed to achieve 3.5% higher precision, 4.9% higher accuracy, with 1.2% increase is computational delay, which makes it highly suitable for real-time clinical use cases

A Review: Movie Character Identification Based on Graph Matching

With the rapid development of movie and television industry a huge amount of movie and television data is being generated every day. To manage this data, efficient and effective technique is required, which understand the video contents and organize it properly, Character identification of movie is challenging problem due to huge variation in the appearance of each character and complex background, large motion, non-rigid deformation, occlusion, huge pose, expression, wearing, clothing, even makeup and hairstyle changes and other uncontrolled condition make the result of face detection and face tracking unreliable

Performance analysis of massive multiple input multiple output for high speed railway

This paper analytically reviews the performance of massive multiple input multiple output (MIMO) system for communication in highly mobility scenarios like high speed Railways. As popularity of high speed train increasing day by day, high data rate wireless communication system for high speed train is extremely required. 5G wireless communication systems must be designed to meet the requirement of high speed broadband services at speed of around 500 km/h, which is the expected speed achievable by HSR systems, at a data rate of 180 Mbps or higher. Significant challenges of high mobility communications are fast time-varying fading, channel estimation errors, doppler diversity, carrier frequency offset, inter carrier interference, high penetration loss and fast and frequent handovers. Therefore, crucial requirement to design high mobility communication channel models or systems prevails. Recently, massive MIMO techniques have been proposed to significantly improve the performance of wireless networks for upcoming 5G technology. Massive MIMO provide high throughput and high energy efficiency in wireless communication channel. In this paper, key findings, challenges and requirements to provide high speed wireless communication onboard the high speed train is pointed out after thorough literature review. In last, future research scope to bridge the research gap by designing efficient channel model by using massive MIMO and other optimization method is mentioned